"""
视觉模型性能优化器
实现GPU加速支持、模型量化和批处理推理机制
"""

import os
import logging
import time
import threading
from typing import List, Dict, Any, Optional, Tuple, Union
from queue import Queue, Empty
from concurrent.futures import ThreadPoolExecutor, Future
import numpy as np
from dataclasses import dataclass
from abc import ABC, abstractmethod

try:
    import torch
    import torch.nn as nn
    from torch.quantization import quantize_dynamic
    TORCH_AVAILABLE = True
except ImportError:
    TORCH_AVAILABLE = False

try:
    import onnxruntime as ort
    ONNX_AVAILABLE = True
except ImportError:
    ONNX_AVAILABLE = False

try:
    import tensorrt as trt
    import pycuda.driver as cuda
    TENSORRT_AVAILABLE = True
except ImportError:
    TENSORRT_AVAILABLE = False


@dataclass
class InferenceRequest:
    """推理请求数据结构"""
    request_id: str
    image: np.ndarray
    query: str
    timestamp: float
    priority: int = 0  # 优先级，数字越小优先级越高


@dataclass
class InferenceResult:
    """推理结果数据结构"""
    request_id: str
    result: str
    processing_time: float
    model_used: str
    success: bool
    error_message: Optional[str] = None


class GPUAccelerator:
    """GPU加速器"""
    
    def __init__(self):
        self.logger = logging.getLogger(__name__)
        self.device_info = self._detect_gpu_devices()
        
    def _detect_gpu_devices(self) -> Dict[str, Any]:
        """检测可用的GPU设备"""
        devices = {
            'cuda_available': False,
            'cuda_devices': [],
            'tensorrt_available': False,
            'onnx_gpu_available': False
        }
        
        # 检测CUDA
        if TORCH_AVAILABLE and torch.cuda.is_available():
            devices['cuda_available'] = True
            devices['cuda_devices'] = [
                {
                    'id': i,
                    'name': torch.cuda.get_device_name(i),
                    'memory': torch.cuda.get_device_properties(i).total_memory,
                    'compute_capability': torch.cuda.get_device_properties(i).major
                }
                for i in range(torch.cuda.device_count())
            ]
            
        # 检测TensorRT
        if TENSORRT_AVAILABLE:
            devices['tensorrt_available'] = True
            
        # 检测ONNX GPU支持
        if ONNX_AVAILABLE:
            available_providers = ort.get_available_providers()
            devices['onnx_gpu_available'] = 'CUDAExecutionProvider' in available_providers
            
        return devices
    
    def get_optimal_device(self, model_type: str) -> str:
        """获取最优设备"""
        if model_type == 'pytorch' and self.device_info['cuda_available']:
            # 选择内存最大的GPU
            best_gpu = max(self.device_info['cuda_devices'], 
                          key=lambda x: x['memory'])
            return f"cuda:{best_gpu['id']}"
        elif model_type == 'onnx' and self.device_info['onnx_gpu_available']:
            return 'cuda'
        elif model_type == 'tensorrt' and self.device_info['tensorrt_available']:
            return 'cuda'
        else:
            return 'cpu'
    
    def optimize_memory_usage(self, device: str):
        """优化GPU内存使用"""
        if device.startswith('cuda') and TORCH_AVAILABLE:
            # 清理GPU缓存
            torch.cuda.empty_cache()
            
            # 设置内存分配策略
            torch.cuda.set_per_process_memory_fraction(0.8)  # 使用80%的GPU内存
            
    def get_device_info(self) -> Dict[str, Any]:
        """获取设备信息"""
        return self.device_info


class ModelQuantizer:
    """模型量化器"""
    
    def __init__(self):
        self.logger = logging.getLogger(__name__)
        
    def quantize_pytorch_model(self, model, quantization_type: str = 'dynamic'):
        """量化PyTorch模型"""
        if not TORCH_AVAILABLE:
            raise RuntimeError("PyTorch not available for quantization")
            
        try:
            if quantization_type == 'dynamic':
                # 动态量化
                quantized_model = quantize_dynamic(
                    model, 
                    {nn.Linear, nn.Conv2d}, 
                    dtype=torch.qint8
                )
            else:
                # 静态量化需要校准数据集，这里暂不实现
                raise NotImplementedError("Static quantization not implemented")
                
            self.logger.info(f"Model quantized using {quantization_type} quantization")
            return quantized_model
            
        except Exception as e:
            self.logger.error(f"Model quantization failed: {e}")
            return model
    
    def quantize_onnx_model(self, model_path: str, output_path: str) -> bool:
        """量化ONNX模型"""
        try:
            from onnxruntime.quantization import quantize_dynamic, QuantType
            
            quantize_dynamic(
                model_path,
                output_path,
                weight_type=QuantType.QUInt8
            )
            
            self.logger.info(f"ONNX model quantized: {output_path}")
            return True
            
        except Exception as e:
            self.logger.error(f"ONNX quantization failed: {e}")
            return False
    
    def estimate_quantization_speedup(self, original_size: int, quantized_size: int) -> Dict[str, float]:
        """估算量化后的性能提升"""
        size_reduction = (original_size - quantized_size) / original_size
        estimated_speedup = 1.5 + size_reduction * 0.5  # 经验公式
        
        return {
            'size_reduction': size_reduction,
            'estimated_speedup': estimated_speedup,
            'memory_savings': size_reduction
        }


class BatchProcessor:
    """批处理推理处理器"""
    
    def __init__(self, batch_size: int = 4, max_wait_time: float = 0.1):
        """
        初始化批处理器
        
        Args:
            batch_size: 批处理大小
            max_wait_time: 最大等待时间(秒)
        """
        self.batch_size = batch_size
        self.max_wait_time = max_wait_time
        self.request_queue = Queue()
        self.result_futures = {}
        self.processing = False
        self.processor_thread = None
        self.logger = logging.getLogger(__name__)
        
    def start_processing(self):
        """启动批处理线程"""
        if not self.processing:
            self.processing = True
            self.processor_thread = threading.Thread(target=self._process_batches)
            self.processor_thread.daemon = True
            self.processor_thread.start()
            self.logger.info("Batch processor started")
    
    def stop_processing(self):
        """停止批处理线程"""
        self.processing = False
        if self.processor_thread:
            self.processor_thread.join(timeout=5.0)
        self.logger.info("Batch processor stopped")
    
    def submit_request(self, request: InferenceRequest) -> Future:
        """提交推理请求"""
        future = Future()
        self.result_futures[request.request_id] = future
        self.request_queue.put(request)
        return future
    
    def _process_batches(self):
        """批处理主循环"""
        while self.processing:
            batch = self._collect_batch()
            if batch:
                self._process_batch(batch)
            else:
                time.sleep(0.01)  # 短暂休眠避免CPU占用过高
    
    def _collect_batch(self) -> List[InferenceRequest]:
        """收集批处理请求"""
        batch = []
        start_time = time.time()
        
        while len(batch) < self.batch_size and (time.time() - start_time) < self.max_wait_time:
            try:
                request = self.request_queue.get(timeout=0.01)
                batch.append(request)
            except Empty:
                if batch:  # 如果已有请求，继续等待
                    continue
                else:  # 如果没有请求，退出
                    break
        
        # 按优先级排序
        batch.sort(key=lambda x: x.priority)
        return batch
    
    def _process_batch(self, batch: List[InferenceRequest]):
        """处理一个批次的请求"""
        # 这里应该调用实际的模型推理
        # 暂时使用模拟处理
        for request in batch:
            try:
                # 模拟批处理推理
                result = self._simulate_batch_inference(request)
                
                # 设置结果
                future = self.result_futures.get(request.request_id)
                if future:
                    future.set_result(result)
                    del self.result_futures[request.request_id]
                    
            except Exception as e:
                future = self.result_futures.get(request.request_id)
                if future:
                    future.set_exception(e)
                    del self.result_futures[request.request_id]
    
    def _simulate_batch_inference(self, request: InferenceRequest) -> InferenceResult:
        """模拟批处理推理"""
        start_time = time.time()
        
        # 模拟处理时间
        time.sleep(0.05)  # 50ms模拟处理时间
        
        processing_time = time.time() - start_time
        
        return InferenceResult(
            request_id=request.request_id,
            result=f"Batch processed result for query: {request.query}",
            processing_time=processing_time,
            model_used="batch_processor",
            success=True
        )


class PerformanceMonitor:
    """性能监控器"""
    
    def __init__(self):
        self.metrics = {
            'total_requests': 0,
            'successful_requests': 0,
            'failed_requests': 0,
            'total_processing_time': 0.0,
            'average_processing_time': 0.0,
            'requests_per_second': 0.0,
            'gpu_utilization': 0.0,
            'memory_usage': 0.0
        }
        self.start_time = time.time()
        self.logger = logging.getLogger(__name__)
    
    def record_request(self, result: InferenceResult):
        """记录请求结果"""
        self.metrics['total_requests'] += 1
        
        if result.success:
            self.metrics['successful_requests'] += 1
        else:
            self.metrics['failed_requests'] += 1
        
        self.metrics['total_processing_time'] += result.processing_time
        self.metrics['average_processing_time'] = (
            self.metrics['total_processing_time'] / self.metrics['total_requests']
        )
        
        # 计算RPS
        elapsed_time = time.time() - self.start_time
        self.metrics['requests_per_second'] = self.metrics['total_requests'] / elapsed_time
    
    def get_gpu_metrics(self) -> Dict[str, float]:
        """获取GPU指标"""
        metrics = {}
        
        if TORCH_AVAILABLE and torch.cuda.is_available():
            for i in range(torch.cuda.device_count()):
                memory_allocated = torch.cuda.memory_allocated(i)
                memory_reserved = torch.cuda.memory_reserved(i)
                memory_total = torch.cuda.get_device_properties(i).total_memory
                
                metrics[f'gpu_{i}_memory_allocated'] = memory_allocated / (1024**3)  # GB
                metrics[f'gpu_{i}_memory_reserved'] = memory_reserved / (1024**3)  # GB
                metrics[f'gpu_{i}_memory_total'] = memory_total / (1024**3)  # GB
                metrics[f'gpu_{i}_utilization'] = memory_allocated / memory_total
        
        return metrics
    
    def get_performance_report(self) -> Dict[str, Any]:
        """获取性能报告"""
        gpu_metrics = self.get_gpu_metrics()
        
        return {
            'basic_metrics': self.metrics,
            'gpu_metrics': gpu_metrics,
            'uptime': time.time() - self.start_time
        }


class VisionModelOptimizer:
    """视觉模型优化器主类"""
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        """
        初始化优化器
        
        Args:
            config: 优化配置
        """
        self.config = config or self._get_default_config()
        self.logger = logging.getLogger(__name__)
        
        # 初始化组件
        self.gpu_accelerator = GPUAccelerator()
        self.quantizer = ModelQuantizer()
        self.batch_processor = BatchProcessor(
            batch_size=self.config.get('batch_size', 4),
            max_wait_time=self.config.get('max_wait_time', 0.1)
        )
        self.performance_monitor = PerformanceMonitor()
        
        # 启动批处理器
        if self.config.get('enable_batch_processing', True):
            self.batch_processor.start_processing()
    
    def _get_default_config(self) -> Dict[str, Any]:
        """获取默认配置"""
        return {
            'enable_gpu_acceleration': True,
            'enable_quantization': True,
            'enable_batch_processing': True,
            'batch_size': 4,
            'max_wait_time': 0.1,
            'quantization_type': 'dynamic',
            'memory_optimization': True
        }
    
    def optimize_model(self, model: Any, model_type: str) -> Any:
        """优化模型"""
        optimized_model = model
        
        try:
            # GPU加速
            if self.config.get('enable_gpu_acceleration', True):
                device = self.gpu_accelerator.get_optimal_device(model_type)
                self.logger.info(f"Using device: {device}")
                
                if model_type == 'pytorch' and device.startswith('cuda'):
                    optimized_model = model.to(device)
                    self.gpu_accelerator.optimize_memory_usage(device)
            
            # 模型量化
            if self.config.get('enable_quantization', True):
                if model_type == 'pytorch':
                    optimized_model = self.quantizer.quantize_pytorch_model(
                        optimized_model, 
                        self.config.get('quantization_type', 'dynamic')
                    )
            
            self.logger.info("Model optimization completed")
            return optimized_model
            
        except Exception as e:
            self.logger.error(f"Model optimization failed: {e}")
            return model
    
    def submit_inference_request(self, image: np.ndarray, query: str, 
                               priority: int = 0) -> Future:
        """提交推理请求"""
        request = InferenceRequest(
            request_id=f"req_{int(time.time() * 1000000)}",
            image=image,
            query=query,
            timestamp=time.time(),
            priority=priority
        )
        
        if self.config.get('enable_batch_processing', True):
            return self.batch_processor.submit_request(request)
        else:
            # 直接处理单个请求
            future = Future()
            try:
                result = self._process_single_request(request)
                future.set_result(result)
            except Exception as e:
                future.set_exception(e)
            return future
    
    def _process_single_request(self, request: InferenceRequest) -> InferenceResult:
        """处理单个请求"""
        start_time = time.time()
        
        # 这里应该调用实际的模型推理
        # 暂时返回模拟结果
        processing_time = time.time() - start_time
        
        result = InferenceResult(
            request_id=request.request_id,
            result=f"Optimized inference result for: {request.query}",
            processing_time=processing_time,
            model_used="optimized_model",
            success=True
        )
        
        # 记录性能指标
        self.performance_monitor.record_request(result)
        
        return result
    
    def get_optimization_info(self) -> Dict[str, Any]:
        """获取优化信息"""
        return {
            'config': self.config,
            'gpu_info': self.gpu_accelerator.get_device_info(),
            'performance_metrics': self.performance_monitor.get_performance_report(),
            'supported_optimizations': {
                'gpu_acceleration': self.gpu_accelerator.device_info['cuda_available'],
                'quantization': TORCH_AVAILABLE or ONNX_AVAILABLE,
                'batch_processing': True,
                'tensorrt': TENSORRT_AVAILABLE
            }
        }
    
    def shutdown(self):
        """关闭优化器"""
        if self.batch_processor:
            self.batch_processor.stop_processing()
        self.logger.info("Vision model optimizer shutdown")


# 工厂函数
def create_vision_optimizer(config: Optional[Dict[str, Any]] = None) -> VisionModelOptimizer:
    """创建视觉模型优化器"""
    return VisionModelOptimizer(config)


# 示例使用
if __name__ == "__main__":
    # 配置日志
    logging.basicConfig(level=logging.INFO)
    
    # 创建优化器
    optimizer = create_vision_optimizer({
        'enable_gpu_acceleration': True,
        'enable_quantization': True,
        'enable_batch_processing': True,
        'batch_size': 8
    })
    
    # 获取优化信息
    info = optimizer.get_optimization_info()
    print("Optimization info:", info)
    
    # 模拟推理请求
    import asyncio
    
    async def test_inference():
        # 创建模拟图像
        test_image = np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8)
        
        # 提交请求
        future = optimizer.submit_inference_request(test_image, "Test query")
        
        # 等待结果
        result = future.result(timeout=5.0)
        print("Inference result:", result)
    
    # 运行测试
    # asyncio.run(test_inference())
    
    # 关闭优化器
    optimizer.shutdown()